Amey-Thakur Decision journals
You cannot improve decisions you cannot evaluate, and you cannot evaluate them honestly after the fact because hindsight rewrites what you knew. A decision journal captures the decision, its reasoning, and its predicted outcome at the time, so later you can compare what happened to what you expected and actually learn.
Method
- Record the decision and its context at the time. What was decided, the situation, the information available, and the alternatives considered (see architecture-decision-records for the technical-architecture instance, tradeoff-analysis for the comparison). Capture this before the outcome is known, because afterward memory reconstructs it to fit what happened.
- Write down the reasoning and the key assumptions. Why this option, what you believed had to be true for it to work, and what would make it wrong: the load-bearing assumptions (see hypothesis-driven-work's falsifiability). When the decision plays out, you check which assumptions held, which is where the learning is.
- Make a falsifiable prediction with confidence. What you expect to happen and how sure you are (a probability or a range): "70% this cuts support tickets by a third within two months". The prediction plus confidence is what makes calibration possible: without it, every outcome feels like what you expected (see estimation-techniques' error bars).
- Note your emotional and situational state. Time pressure, who was pushing, how you felt: these shape decisions and reveal patterns (you decide worse under deadline, or defer too readily to the loudest voice: see receiving-feedback's self-model). The journal surfaces these biases across many entries.
- Review at the outcome, honestly. When results are in, compare to the prediction: right for the right reasons, right by luck, wrong despite good process, or wrong because of a flaw you can name? Separate decision quality from outcome quality: a good decision can have a bad outcome (variance) and vice versa; judging decisions by outcomes alone (outcome bias) learns the wrong lessons.
- Aggregate for calibration and patterns. Across many entries: are your 70%-confident predictions right about 70% of the time (calibration)? Do certain decision types or states correlate with bad outcomes? The compounding value is not any single review but the pattern over dozens, which turns vague "I've gotten better at this" into measured improvement.
Boundaries
- The journal captures decisions worth learning from (consequential, uncertain, recurring types), not every trivial choice; over-journaling is its own procrastination.
- Honesty is the whole mechanism; a journal written to look good in hindsight, or reviewed defensively, teaches nothing. The value requires admitting wrong predictions and bad reasoning (see the ego separation in receiving-feedback).
- Decision journaling improves individual and team calibration over time; it does not make any single hard decision easy (that is tradeoff-analysis, hypothesis-driven-work). It is a long-game learning tool.